Diagnosing and monitoring CNS malignancies using microRNA

ABSTRACT

The use of specific microRNAs (miRNAs) present in CSF as biomarkers for particular brain malignancies and disease activity.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No. 14/875,367, filed Oct. 5, 2015, which is a continuation of U.S. patent application Ser. No. 13/885,762, filed May 16, 2013, which has a 371 (c) date of Sep. 3, 2013. U.S. patent application Ser. No. 13/885,762 is a U.S. National Phase Application under 35 U.S.C. § 371 of International Patent Application No. PCT/US2011/061047, filed on Nov. 16, 2011, which claims the benefit of U.S. Provisional Patent Application Ser. No. 61/457,000, filed on Nov. 16, 2010. The entire contents of the foregoing are hereby incorporated by reference in their entireties.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant Nos. CA023100, CA124804, and CA138734 awarded by the National Institutes of Health. The Government has certain rights in the invention.

TECHNICAL FIELD

The present methods relate to the use of specific microRNAs (miRNAs) that are present in CSF as biomarkers for particular brain malignancies and disease activity.

BACKGROUND

The most frequently occurring brain malignancies in adults are metastatic brain cancers (e.g., from primary melanoma, lung cancer, breast cancer, gastrointestinal cancer (e.g., pancreatic or colorectal), kidney cancer, bladder cancer, certain sarcomas, or testicular or germ cell tumors) followed by glioblastoma (GBM). GBM is the most aggressive primary brain cancer, which generally has a poor prognosis with median survival of about 14 months, despite aggressive treatment (Filippini et al. Neuro Oncol. 2008; 10(0:79-87). Currently diagnosis of brain tumors is made with brain biopsy if possible and the analysis of cerebrospinal fluid (CSF) for the presence of cancer cells (cytology). CSF can be accessed readily for longitudinal disease monitoring during and after therapy. However, the currently used method of CSF analysis has moderate sensitivity, is non-quantitative and technically challenging. There is presently no routine way to subtype the malignancy and monitor molecular changes from CSF indicating the need for more accurate and reliable biomarkers and methods.

SUMMARY

The present invention is based on the identification of specific miRNAs that can serve as biomarkers for particular brain malignancies and disease activity.

Thus, in a first aspect, the invention provides methods for detecting or making a diagnosis between metastatic and primary brain tumors. The methods include determining levels of miR-10b, miR-21, and miR-200 in a sample from a subject, and comparing the levels of miR-10b, miR-21, and miR-200 to reference levels of miR-10b, miR-21, and at least one miR-200 family member. The presence of levels of all of miR200, miR-10b or miR-21 below the reference levels indicates the absence of a metastatic or primary brain tumor. The presence of levels of miR-10b or miR-21 above the reference levels indicates the presence of a metastatic or primary brain tumor. The presence of levels of the miR-200 family member above the reference level indicates the presence of a metastatic brain tumor.

In another aspect, the invention provides computer-implemented methods for detecting or making a diagnosis between metastatic and primary brain tumors. The methods include determining levels of miR-10b, miR-21, and at least one miR-200 family member, in a sample from a subject, to provide a subject dataset; downloading the dataset into a computer system having a memory, an output device, and a processor programmed for executing an algorithm, wherein the algorithm assigns the datasets into one of two categories levels of miR-10b, miR-21, and at least one miR-200 family member; assigning the subject dataset into the first or second category; and generating an output comprising a report indicating the assignment to the first or second category.

In some embodiments, the first category is presence of a primary brain tumor and the second category is presence of a metastatic brain tumor. In some embodiments, an assignment to the first category is made based on the presence of levels of miR-10b or miR-21 above reference levels, and the presence of levels of the miR-200 family member below a reference level; and an assignment to the second category is made based on the presence of levels of miR-10b or miR-21 above reference levels, and the presence of levels of the miR-200 family member above the reference level.

In some embodiments, the first category is presence of a primary brain tumor or a metastatic brain tumor, and the second category is absence of a primary brain tumor or a metastatic brain tumor. In some embodiments, an assignment to the first category is made based on the presence of any of miR200, miR-10b or miR-21 above reference levels, and an assignment to the second category is made based on the presence of levels of all of miR200, miR-10b or miR-21 below the reference levels.

In some embodiments, the algorithm is a linear algorithm or radial basis function.

In some embodiments, the algorithm is a linear algorithm comprising: (a*miR-125b)+(b*miR-10b)+(c*miR-21)+(d*miR-141)+(e*miR-200a)+(f*miR-200b)+(g*miR-200c)−h, wherein a-g are weights and his a constant, determined using a support vector machine algorithm.

In some embodiments, the methods further include selecting a treatment for a metastatic or primary brain tumor for the subject, based on the presence of a metastatic or primary brain tumor.

In some embodiments, the methods further include administering the treatment to the subject.

In another aspect, the invention provides methods for monitoring progression of a brain tumor. The methods include determining levels of one or more of miR-10b, miR-21, and a miR-200 family member in a first sample; and determining levels of one or more of miR-10b, miR-21, and a miR-200 family member in a subsequent sample. The presence of levels of miR-10b, miR-21, or miR-200 family member in the subsequence sample above the levels in the first sample indicates the presence of progression or recurrence of the brain tumor, and levels of miR-10b miR-21, or miR-200 family member in the subsequent sample below the levels in the first sample indicates that the brain tumor is regressing or is in remission.

In some embodiments, wherein the subject has been diagnosed with a primary brain tumor, the methods include monitoring levels of one or both of miR-10b and miR-21. In some embodiments, wherein the subject has been diagnosed with a metastatic brain tumor, the methods include monitoring levels of one or more of miR-10b, miR-21, and a miR-200 family member.

In some embodiments, the methods further include administering a treatment to the subject, e.g., between the first and subsequent samples, and a decrease in levels of miR-10b, miR-21, or at least one miR-200 family member in the subsequence sample as compared to the level in the first sample indicates that the treatment was effective, e.g., reduced the size of the tumor. No change indicates that the treatment either halted tumor growth or had no effect, and an increase indicates that the treatment was not effective.

In some embodiments, the treatment includes administration of one or more of surgical resection, chemotherapy, or radiotherapy.

In some embodiments of the methods described herein, the sample comprises cerebrospinal fluid from a subject.

In some embodiments of the methods described herein, the subject is a human who has or is suspected of having a brain tumor.

In some embodiments of the methods described herein, the levels are determined using RT-PCR.

In some embodiments of the methods described herein, the miR-200 family member is miR-200a, miR-200b, miR-200c, miR-141, or miR-429.

In some embodiments of the methods described herein, the method comprises normalizing the levels to a level of a housekeeping miRNA, e.g., miR-125 or miR-24.

In some embodiments of the methods described herein, the primary brain tumor is a glioma, glioblastoma, hemangioma, or medulloblastoma.

In some embodiments of the methods described herein, the metastatic brain tumor is from a primary lung, breast, kidney, bladder, testicular, germ cell or gastrointestinal cancer, or melanoma.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIGS. 1A-C show miR-10b and miR-21 up-regulation in GBM, and CSF levels of miR-10b and miR-21 in patients with GBM, metastatic brain cancer and non-neoplastic controls. (1A) miRNAs deregulated in GBM more than two fold as compared to normal brains. miRNA levels were obtained by the analysis of TCGA miRNA microarrays data and error bars represent standard deviation between individual probe sets present for each miRNA on the arrays. (1B) miR-10b and (1C) miR-21 levels were examined by qRT-PCR in CSF samples of neurological patients, and the relative levels are demonstrated for individual CSF samples. The lines indicate median miRNA levels for each group of patients: “Controls”—non-neoplastic neuropathological cases, “GBM”—glioblastoma cases, “Breast to Brain” and “Lung to Brain”—breast and lung cancer brain metastasis, “Breast LM” and “Lung LM”—breast and lung cancer leptomeningeal metastasis, respectively. Differences between group means have been determined by non-parametric Wilcoxon Signed Rank test and the significance is indicated by asterisks: (*) p<0.05, (**) p<0.001, (***) p<0.0001. miR-10b and miR-21 CSF levels normalized to miR-125b are presented in FIGS. 6A-B.

FIGS. 2A-F show the results of detection of miRNAs of miR-200 family in metastatic brain cancer patients. (2A) miR-200b expression levels were examined by qRT-PCR in various primary and metastatic brain tumor tissue specimens and normalized to ubiquitously expressed miR-125b. Error bars indicate standard errors between technical duplicates. PNET: primitive neuroectodermal brain tumor. MiR-200a (2B), miR-200b (2C), miR-200c (2D) and miR-141 (2E) levels were examined by qRT-PCR in CSF samples of neurological patients, and the relative values are demonstrated for individual patients. Differences between group means that reached statistical significance as determined by non-parametric Wilcoxon Signed Rank test are indicated with asterisks: (*) p<0.05, (**) p<0.001, (***) p<0.0001. Corresponding values normalized to miR-125b are presented in Suppl. FIG. 2 C-F. (2F) The average levels of miR-200a/miR-200b and miR-141/miR-200c cluster miRNAs in CSF of metastatic brain cancer patients. The error bars represent the standard error of mean for each group of patients.

FIG. 3A is an exemplary diagnostic decision tree showing a method of classification of brain cancer patients based on CSF miRNA biomarkers (miR-10b, -21, and -200).

FIG. 3B is a pair of graphs showing the correlation of miR-10b and miR-21 levels between brain tumors and matching CSF samples collected from the same patients. The Pearson coefficients (r) of linear regression between two data sets were calculated for each miRNA.

FIGS. 4A-C show CSF levels of miRNA markers in metastatic lung cancer and GBM patients during treatment with erlotinib. miRNAs levels were examined by qRT-PCR in CSF samples of lung cancer patients (Patients A, C) and GBM patient (Patient B) during the time course of erlotinib treatment. The disease progression and the drug response were concomitantly monitored by MRI, as following. For Patient A (shown in FIG. 4A): serial axial post-gadolinium MRIs of lung cancer patient's brain during course of progression of disease and stability and improvement on MRI with escalating doses of erlotinib. A: time 0 weeks while patient on erlotinib, there is no leptomeningeal and parenchymal enhancement and CSF cytology was negative; B: 3 weeks progression on erlotinib 150 mg daily dosing with new cerebellar leptomeningeal enhancement (small arrows) and nodule (large arrow), erlotinib increased to 600 mg every 4 days at 9 weeks; C: 29 weeks on showing stable leptomeningeal enhancement and nodule; D-40 weeks showing reduction in leptomeningeal enhancement and nodule, erlotinib increased to 900 mg every 4 days at 41 weeks; E: 64 weeks after 6 cycles of chemotherapy with carboplatinum and pemetrexed due to lung cancer progression showing further reduction in leptomeningeal enhancement and nodule has disappeared. For Patient B (shown in FIG. 4B): A: time 2 weeks for patient with GBM with predominant mass effect and enhancement felt to be radiation changes rather than tumor based on MM spectroscopy and PET scan on erlotinib at 600 mg every 4 days; B: 26 weeks on treatment showing progression on MRI with new lesion (arrow) concerning for tumor; C: 27 weeks on treatment showing hypermetabolic area (arrow) on PET consistent with tumor and biopsy confirmed. For Patient C (shown in FIG. 4C): had inadequate treatment due to functional status and rapidly progressed over a few weeks, which was reflected by an increase in levels of miR-200 family members in a short interval.

FIGS. 5A-G are graphs showing miR-NA levels in CSF of patients with GBM, metastatic brain cancers and non-neoplastic neurological conditions. miR-NA levels were determined in CSF samples by qRT-PCR and relative levels calculated by ΔCt method with expression at Ct=36 set as one unit.

FIGS. 6A-F are graphs showing miRNA levels in CSF of randomly selected patients with GBM, metastatic brain cancers and non-neoplastic controls are demonstrated for: (6A) miR-15b, (6B) miR-15b normalized to miR-125b, (6C) miR-17-5p, (6D) miR-17-5p normalized to miR-125b, (6E) miR-93, (6F) miR-93 normalized to miR-125b. miRNA levels in CSF samples were determined by qRT-PCR reaction. Relative miRNA levels were quantified by the ΔCt method and normalized to miR-125b as described in Materials and methods. Error bars represent standard error of mean between technical duplicates.

FIGS. 7A-B are bar graphs showing miR-10b expression in different human tissues. (7A) The normalized data on miR-10b expression in various human tissues were obtained from qRT-PCR based profiling (Liang, 2007). miR-10b levels were calculated relative to miR-10b expression in brain, which was set as one unit. (7B) The data on miR-10b expression in normal human tissues and corresponding carcinomas were obtained from profiling based on hybridization arrays (Lu, 2005), Gene Expression Omnibus (GEO) accession number GSE2564. Normalized miR-10b signals were plotted relative to miR-10b level in brain, which was set as one unit.

FIGS. 8A-B are bar graphs showing miRNA-200 family in different human tissues. (8A) The normalized data on miR-200a, -200b, 200c and miR-141 expression in human tissues were obtained from qRT-PCR based profiling (Liang, 2007). miRNA levels were calculated relative to corresponding miRNA expression levels in brain, which were set as one unit. (8B) The data on miR-200 family expression in normal human tissues and corresponding carcinomas were obtained from profiling based on hybridization arrays (Lu, 2005); Gene Expression Omnibus (GEO) accession number GSE2564. Normalized signals for specific miRNAs were plotted relative to corresponding miRNA levels in brain, which were set as one unit.

FIG. 9. miR-195 levels in CSF of randomly selected patients with GBM, metastatic brain cancers and non-neoplastic controls. miR-195 levels in CSF samples were determined by qRT-PCR reaction. Relative miRNA levels were quantified by ΔCt method as described. Error bars represent standard error of mean between technical duplicates.

FIGS. 10A-F are graphs showing miRNA levels in CSF of patients with GBM and metastatic brain cancers remissions. The levels of (10A) miR-10b, (10B) miR-21, (10C) miR-200a, (10D) miR-200b, (10E) miR-200c and (10F) miR-141 were determined in CSF by qRT-PCR reaction. Relative miRNA levels were quantified by ΔCt method and normalized to miR-125b as described in Materials and methods. Average miRNA levels are presented for each group of cancer patients and individual miRNA levels are presented for patients with cancer remissions. Error bars represent standard error of mean within groups of patients.

DETAILED DESCRIPTION

miRNAs are small endogenous mediators of RNA interference and key regulatory components of many biological processes required for organism development, cell specialization and homeostasis. Many miRNAs exhibit tissue-specific patterns of expression and are deregulated in various cancers, where they can either be oncogenic (oncomirs) or tumor suppressive. The recent discovery of miRNAs in the secreted membrane vesicles, exosomes^(2, 3), as well as in the blood serum^(4, 5) and other body fluids⁶ suggested that miRNAs play a role in intercellular communication in both paracrine and endocrine manner. It had also opened a new exciting direction for study of miRNAs as biomarkers for diseases, and cancer diagnostics by miRNA profile in blood serum became a quickly growing field⁷.

Several studies have reported miRNA detection, among several biological fluids, in CSF⁸⁻¹⁰, raising the possibility that miRNAs in CSF might serve as informative biomarkers of central nervous system (CNS) disease. Such a possibility, largely unexplored until now, is supported by the finding that different types of brain cancer have distinct signatures of miRNA expression, with some miRNAs species abundant in cancer while undetectable in healthy brain¹¹⁻¹³ Since CSF is separated from blood circulation by blood-brain barrier, it is conceivable that CSF might better retain a unique signature of miRNA expression specific for brain tumors.

A recent study demonstrated the usefulness of miRNA profiling in CSF for diagnostics of brain lymphoma¹⁰. In the current study, levels of several candidate miRNAs were tested in the CSF of patients with GBM and compared to those of metastatic brain cancers and a variety of non-neoplastic CNS diseases. There was a strong association between the particular types of brain cancer and the presence of specific miRNAs in CSF. Using this approach enables detection of GBM and metastatic brain cancers, and discrimination between them with about 95% accuracy. These results demonstrate the utility of miRNA as biomarkers of high-grade brain malignancies and reveal their value for the development of diagnostic and prognostic tools, as well as for monitoring of CNS pathology in general.

Methods of Diagnosis/Detection of CNS Malignancies

Thus, the methods described herein can be used to diagnose, i.e., detect the presence of, a CNS malignancy, based on levels of miRNAs in CSF, e.g., levels of one or more of miR-21, miR-10b, and or a miR-200 (as used herein, the term “miR-200” encompasses all members of the miR-200 family, i.e., miR-200a, miR-200b, miR-200c, miR-141, and miR-429). In some embodiments, levels of miR-10b are determined and compared to a reference level, and the presence of levels of miR-10b in the subject above the reference level indicates that the subject has a metastatic or neoplastic primary brain tumor, e.g., GBM. In some embodiments, levels of miR-200 are determined and compared to a reference level, and the presence of levels of miR-200 (e.g., miR-200a) in the subject above the reference level indicates that the subject has a metastatic brain tumor, e.g., from primary lung or breast cancer. In some embodiments, levels of miR-21 are determined and compared to a reference level, and the presence of levels of miR-21 in the subject above the reference level indicates that the subject has a metastatic or neoplastic primary brain tumor, e.g., GBM. In some embodiments, the methods include determining levels of miR-10b or miR-21 and miR-200 (either non-normalized or normalized to relatively uniformly expressed miRNAs such as miR-125 or miR-24), and comparing the levels of each miRNA to a reference level. In this case, the presence of elevated miR-10b or miR-21 indicates the presence of a metastatic or neoplastic primary brain tumor, e.g., GBM, and the presence of miR-200 indicates the presence of a metastatic brain tumor. See, e.g., FIG. 3A.

In some embodiments, the methods are used to determine whether a metastatic brain tumor originated from a primary breast or lung tumor. The methods include detecting levels of miR-200a and/or miR-200b. The presence of increased levels of miR-200a and miR-200b (two miRNAs encoded as a cluster at chromosome 1p36.33) in CSF indicate an increased likelihood of the presence of metastatic breast cancer relative to lung cancer. In some embodiments, the methods include determining CSF levels of miR-141 and -200c (co-encoded at chromosome 12p13.31), which are expressed at similar levels in breast and lung cancer cases, and determining a ratio between the miRNAs of the two different miR-200 genomic clusters (e.g., [level of miR200a+level of miR200b]/[level of miR141+miR200c], wherein a ratio above a reference ratio indicates an increased likelihood of the presence of metastatic breast cancer relative to lung cancer.

In some embodiments, the methods are used to make a differential diagnosis of GBM versus brain metastasis, or GBM and brain metastasis versus non-neoplastic tumors on the basis of detection of levels in a CSF sample of seven miRNAs: miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c as independent variables. Each of these miRNAs is detected in the sample, and an algorithm (e.g., a linear or radial is applied to make a diagnosis.

Reference levels can be determined using methods known in the art, e.g., standard epidemiological and biostatistical methods. The reference level can represent the levels in a reference cohort, e.g., levels in subjects who do not have GBM or metastatic brain cancer. The reference levels can be, e.g., median levels, or levels representing a cutoff for the highest quartile, and can be set to provide sufficient specificity and accuracy to provide for an optimal level of true positives/true negatives while minimizing levels of false positives/false negatives. Appropriate methods are known in the art. See, e.g., Fleiss, “Design and Analysis of Clinical Experiments,” (Wiley-Interscience; 1 edition (Feb. 22, 1999)); Lu and Fang, “Advanced Medical Statistics,” (World Scientific Pub Co Inc (Mar. 14, 2003)); Armitage et al., “Statistical Methods in Medical Research, 4^(th) Ed”, Blackwell Science (Boston, Mass., Oxford: Blackwell Scientific Publications, 2001).

In some embodiments, the methods include determining levels of miR-125b, and normalizing levels of other miRNAs to the levels of miR-125b, see, e.g., FIGS. 5A-5G. The reference levels can then be set in comparison to those normalized levels, using methods known in the art.

In some embodiments, miRNA levels are determined after an initial diagnosis of a brain mass, e.g., detection of a mass using an imaging method such as MM, or after a subject has presented with symptoms that are consistent with a brain mass, to assist in making a differential diagnosis of GBM versus brain metastasis versus non-neoplastic tumor. A health care provider can identify subjects who have symptoms consistent with a brain mass based on knowledge in the art; general signs and symptoms include new onset or change in pattern of headaches; headaches that gradually become more frequent and more severe; unexplained nausea or vomiting; vision problems, such as blurred vision, double vision or loss of peripheral vision; gradual loss of sensation or movement in an arm or a leg; difficulty with balance; speech difficulties; confusion in everyday matters; personality or behavior changes; seizures, especially in someone who doesn't have a history of seizures; and hearing problems.

In some embodiments, once a differential diagnosis is made, the methods include the selection and optionally the administration of a treatment for the diagnosed disease. Thus, the methods can include selecting a treatment regimen for the subjects comprising one or more of surgical intervention, chemotherapy, and radiotherapy. For all brain cancers, the choice of therapy (e.g., surgery, radiation and/or chemotherapy) can be chosen depending on site, size, neurological function, and systemic disease status. For example, if the subject has GBM, then a treatment regime including radiation, temozolamide, and avastin may be selected and optionally administered. If the subject has metastatic brain cancer, then the treatment may depend on the source of the metastasis, i.e., on the primary cancer. For metastatic breast cancer, then the treatment could include chemotherapies approved for breast cancer (e.g., herceptin, lapatinib, doxil, or taxanes); for lung metastases, then lung cancer therapies can be selected (e.g., tarceva, alimta, or carboplatin). One of skill in the art would be able to select an appropriate treatment based on knowledge in the art. See, e.g., the National Comprehensive Cancer Network (NCCN) Guidelines, available on the internet at nccn.org.

For a subject who has been determined to have a non-neoplastic lesion using a method described herein, the methods can include monitoring the subject on a continuing basis to detect any change in the lesion, e.g., a shift to malignancy, which would be indicated by an increase in levels of miR-10b, miR-21, or miR-200.

Methods of Monitoring CNS Malignancies

The methods described herein can also be used to monitor a subject, e.g., a subject who is undergoing treatment or being followed for progression. The methods include determining levels of miR-10b, miR-21, and/or miR-200, wherein the presence of levels of miR-10b, miR-21, or miR-200 above a reference level indicate the presence of recurrence of the malignancy, and levels below the reference level indicate that the subject is in remission.

In some embodiments, e.g., for a subject who is undergoing treatment, levels of miR-10b, miR-21, and/or miR-200 can be monitored over time (e.g., by comparing levels determined from first and second, e.g., subsequent, samples taken over time; the first sample can be, but need not be, a baseline or initial sample); a decrease in levels of miR-10b, miR-21, and/or miR-200 in a subject undergoing treatment indicates that the treatment is effective. An increase in levels indicates progression. No significant change in levels indicates that no significant change has occurred, i.e., no significant change in a subject being treated that the treatment is at best slowing growth of the tumor, or is ineffective, and no significant change in a subject who is not being treated indicates that the tumor is not progressing. The presence of elevated levels in a subject who was previously in remission indicates the presence of a recurrence of the tumor, and can indicate a need for treatment.

In addition, the methods can be used to detect real progression versus pseudoprogression (a phenomenon in which a subject is observed to have experienced disease growth immediately after therapy, e.g., after radiotherapy, but are later shown to have improved or stable disease by brain imaging, see, e.g., Hoffman et al., J Neurosurg 50:624-628, 1979; Brandes et al., Clin Oncol 26:2192-2197, 2008; de Witt et al., Neurology 63:535-537, 2004; Taal et al., Cancer 113:405-410, 2008), e.g., in subjects with GBM. In the case of an apparent progression (e.g., as measured by imaging), the presence of stable or decreasing levels of miR-10b (or miR-200) as compared to earlier levels (e.g., pre-treatment levels) indicates that the apparent progression is a pseudoprogression.

The levels can be determined, e.g., before, during, or after treatment, e.g., treatment with surgery (e.g., resection or debulking), chemotherapy, or radiotherapy.

Methods of Detection

Any methods known in the art can be used to detect and/or quantify levels of a miRNA as described herein. For example, the level of a miRNA can be evaluated using methods known in the art, e.g., RT-PCR (e.g., the TAQMAN miRNA assay or similar), quantitative real time polymerase chain reaction (qRT-PCR), Northern blotting, RNA in situ hybridization (RNA-ISH), RNA expression assays, e.g., microarray analysis, deep sequencing, cloning or molecular barcoding (e.g., NANOSTRING, as described in U.S. Pat. No. 7,473,767). Analytical techniques to determine miRNA levels are known. See, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y. (2001).

In some embodiments, the methods include contacting an agent that selectively binds to a biomarker, e.g., to a miRNA (such as an oligonucleotide probe that binds specifically to the miRNA) with a sample, to evaluate the level of the miRNA in the sample. In some embodiments, the agent bears a detectable label. The term “labeled,” with regard to an agent encompasses direct labeling of the agent by coupling (i.e., physically linking) a detectable substance to the agent, as well as indirect labeling of the agent by reactivity with a detectable substance. Examples of detectable substances are known in the art and include chemiluminescent, fluorescent, radioactive, or colorimetric labels. For example, detectable substances can include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride, quantum dots, or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include ¹²⁵I, ¹³¹I, ³⁵S or ³H.

In some embodiments, high throughput methods, e.g., arrays (e.g., TAQMAN Array MicroRNA Cards) or gene chips as are known in the art (see, e.g., Ch. 12, “Genomics,” in Griffiths et al., Eds. Modern genetic Analysis, 1999, W. H. Freeman and Company; Ekins and Chu, Trends in Biotechnology, 1999; 17:217-218; MacBeath and Schreiber, Science 2000, 289(5485):1760-1763; Hardiman, Microarrays Methods and Applications: Nuts & Bolts, DNA Press, 2003), can be used to detect the presence and/or level of a miRNA.

In some embodiments, the methods include using a modified RNA in situ hybridization technique using a branched-chain DNA assay to directly detect and evaluate the level of a miRNA in the sample (see, e.g., Luo et al., U.S. Pat. No. 7,803,541B2, 2010; Canales et al., Nature Biotechnology 24(9):1115-1122 (2006); Nguyen et al., Single Molecule in situ Detection and Direct Quantiication of miRNA in Cells and FFPE Tissues, poster available at panomics.com/index.php?id=product_87). A kit for performing this assay is commercially-available from Affymctrix (VicwRNA).

Human miRNA Sequences

The following table sets forth sequences for mature human miRNAs useful in the present methods.

SEQ ID Micro RNA NO: Mature Sequence miR-10b  1 UACCCUGUAGAACCGAAUUUGUG miR-21  2 UAGCUUAUCAGACUGAUGUUGA miR-24-1  3 UGCCUACUGAGCUGAUAUCAGU miR-24-2  4 UGCCUACUGAGCUGAAACACAG miR-200a  5 CAUCUUACCGGACAGUGCUGGA miR-200b  6 CAUCUUACUGGGCAGCAUUGGA miR-200c  7 CGUCUUACCCAGCAGUGUUUGG miR-141  8 CAUCUUCCAGUACAGUGUUGGA miR-429  9 UAAUACUGUCUGGUAAAACCGU miR-125 10 UCCCUGAGACCCUAACUUGUGA

Algorithms and Computer-Implemented Methods

In some embodiments, the methods include using one or more algorithms to assign a diagnosis, based on levels of miRNAs as described herein. For example, the methods can include the use of a linear algorithm, in which one or more of the levels are weighted. In another example, the methods can include the use of a radial basis function (RBF). Appropriate linear and RBF algorithms useful in the present methods can be generated using methods known in the art, e.g., a support vector machine (SVM). The SVM was originally developed by Boser, Guyon and Vapnik (“A training algorithm for optimal margin classifiers”, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, ACM (1992) pp. 142-152). See, e.g., Vapnik, “Statistical Learning Theory.” John Wiley & Sons, Inc. 1998; Cristianini and Shawc-Taylor, “An Introduction to Support Vector Machines and other kernel-based learning methods.” Cambridge University Press, 2000. ISBN 0-521-78019-5; and Schölkopf and Smola, “Learning with Kernels.” MIT Press, Cambridge, Mass., 2002, as well as U.S. Pat. Nos. 7,475,048 and 6,882,990, all of which are incorporated herein by reference in their entirety for their teachings relating to computer systems and SVM-based methods. For example, the present methods can be performed using a computer system as described in FIG. 4 of U.S. Pat. No. 7,475,048.

EXAMPLES

The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

Materials and Methods

The following materials and methods were used in Examples 1-5, below.

Collection of Samples.

CSF and brain tumor samples were obtained from the Department of Neurosciences, UC San Diego, Moores Cancer Center, La Jolla, Calif., Department for Neurosurgery at Brigham and Women's Hospital, Boston, Mass., and from the Department for Neurosurgery at Gottingen University Medical Center, Gottingen, Germany over the period of 2-5 years. At least one ml of each CSF sample was cleared of cells and debris immediately after collection by brief centrifugation at 3000 rpm 5 min at 4° C. and stored in aliquots at −80° C. All tumor specimens were fresh-frozen on dry ice and stored at −80° C. until tested.

RNA Isolation and miRNA Profiling.

CSF samples were lyophilized and total RNA was extracted using mirVana miRNA isolation kit (Ambion) according to the manufacturer's protocol. The amount of RNA extracted from the CSF samples was within 50-2500 ng/ml range, consistent with the previous findings³. Total RNA from frozen tumor tissues was isolated using Trizol reagent (Invitrogen). The levels of individual miRNAs in CSF and tumors were determined by TaqMan miRNA assays from Applied Biosystems. Four ng of total RNA was used in 6 μl of reverse transcription reaction with specific miRNA RT probes, prior to TaqMan real-time PCR reactions that were performed in duplicates. MiR-125b, which is abundantly and uniformly expressed in brain, was detected in all CSF samples and used as an internal control for normalization (FIG. 5). However, since miR-125b levels themselves are not uniform across the CSF samples, both normalized and non-normalized data were considered in this study. No miRNA marker that was less variable across the CSF samples was identifiable, and generally higher miRNA CSF levels were observed in neoplastic cases relative to non-neoplastic controls. This trend may reflect a release of miRNA-containing microvesicles by cancer cells³ and/or destruction of the brain tissue in neoplastic conditions. miRNAs levels were calculated relative to corresponding miR-125b levels by the formula 2^ΔCt, where ΔCt=Ct_(miR-125b)-Ct_(miR-x). All data are mean of technical duplicates, and the standard errors of mean were calculated between duplicates. Normalization to another housekeeping miRNA, miR-24, did not change the results (data not shown).

Samples Classification and Data Analysis.

A total of 118 patients of two neurooncological clinics, and corresponding CSF samples were analyzed in this study. 108 patients were classified into six groups based on clinical and pathological diagnoses (including CSF cytology and tumor histology when applicable), and magnetic resonance imaging (MRI) findings (Table 1A, the detailed patients' characteristics are listed in Table 1B). The first control group referred as “Non-neoplastic” includes patients with various neurological conditions other than brain neoplasia. The patients in this group had no cancer at the time of CSF collection, and no previous history of CNS malignancies. The second group “GBM” includes patients diagnosed with active GBM. GBM was referred to as clinically “active” when primary tumor mass was apparent by MRI imaging at the time of CSF samples collection and was further classified as GBM by tumor tissue histology. The two groups called “Breast to Brain” and “Lung to Brain” comprise of samples from the patients with parenchymal brain metastasis from breast carcinoma and lung cancer (including SCLC and NSCLC), respectively. The presence of metastases in these patients was confirmed by MRI imaging at the time of CSF collection. Two additional groups represent patients with documented leptomeningial metastasis of these cancers (CSF or MRI positive disease). Additional seven patients not included in the groups described above were analyzed separately. These patients represent cases of remission of primary and metastatic brain tumors, as indicated by no detectable brain tumor at the time of CSF collection based on imaging features, clinical stability and CSF cytology. The remaining three patients were analyzed in the longitudinal study.

TABLE 1A Groups of patients included in this study Group N Clinical/Pathology based diagnosis Control 15 Non-neoplastic neurological conditions: headache (4)*, trigeminal neuralgia, memory problem, gait difficulty, dementia, Parkinson disease, myelitis (2), normal pressure hydrocephalus, encephalitis, neuropathy, benign cerebellal lesion, Hodgkin disease with no CNS cancer. GBM 19 Glioblastoma multiforme (glioma grade IV) Breast to 16 Breast cancer metastasis to brain Brain Breast LM 26 Breast cancer leptomeningial metastasis Lung to 28 Lung cancer metastasis to brain Brain Lung LM 4 Lung cancer leptomeningial metastasis N = number of patients per group. *The number of patients with a particular diagnosis, if more than one, is indicated in parenthesis.

TABLE 1B Neurological diagnosis and individual characteristics of patients included in CSF microRNA analysis Year of Clinical/Pathology Tumor CSF sample Time/way of sample ## based diagnosis grade cytology Age Gender collection collection Control (Non-neoplastic neurological conditions) 1 Non-specific pain No Negative 50 F 2005 No surgery/LP syndrome tumor 2 Headache No Negative 33 F 2006 No surgery/LP tumor 3 Memory No Negative 77 F 2006 No surgery/LP problems, gait tumor difficulty 4 Trigeminal No Negative 67 F 2005 No surgery/LP neuralgia tumor 5 Normal pressure No Negative 80 M 2006 No surgery/LP hydrocephalus tumor 6 Benign cerebellar No Negative 60 M 2006 Year after surgery/LP lesion tumor 7 Hodgkin's No Negative 33 F 2007 No surgery/LP disease, no CNS tumor cancer 8 Neuropathy No Negative 28 F 2007 No surgery/LP tumor 9 Encephalitis in No Negative 63 M 2007 No surgery/LP patient with tumor leukemia 10 Dementia No Negative 44 F 2007 No surgery/LP progressive tumor 11 Headache No Negative 25 M 2005 No surgery/LP tumor 12 Headache No Negative 40 F 2007 No surgery/LP tumor 13 Parkinson Disease No Negative 71 M 2008 No surgery/LP tumor 14 Transverse No Negative 43 F 2008 No surgery/LP myelitis tumor 15 Transverse No Negative 31 F 2008 No surgery/LP myelitis tumor GBM: Glioblastoma multiforme 1 GBM IV Negative 55 F 2007 After surgery/LP/ before chemoradiation 2 GBM IV Positive 27 F 2007 After surgery/Ommaya/ after chemoradiation 3 GBM IV Positive 25 F 2008 After surgery/LP/after chemoradiation 4 GBM IV Negative 28 M 2007 After surgery/LP/after chemoradiation 5 GBM IV Positive 59 M 2007 After surgery/LP/after chemoradiation 6 GBM IV Negative 32 M 2007 After surgery/LP/after chemoradiation 7 GBM IV Negative 61 F 2008 After surgery/LP/after chemoradiation 8 GBM IV Negative 63 M 2009 After surgery/LP/after chemoradiation 9 GBM IV NA NA NA 2008 During surgery/ Ommaya/ before chemoradiation 10 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 11 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 12 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 13 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 14 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 15 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 16 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 17 GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 18 GBM IV Negative 61 F 2005 After surgery/LP/after chemoradiation 19 GBM IV NA 43 F 2010 After surgery/LP/ before chemoradiation Breast to Brain: breast cancer brain metastasis 1 Breast carcinoma IV Positive 55 F 2008 No surgery/LP/after brain metastasis radiation and during chemotherapy 2 Breast carcinoma IV Positive 63 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 3 Breast carcinoma IV Positive 54 F 2008 No surgery/LP/after brain metastasis radiation and during chemotherapy 4 Breast carcinoma IV Positive 60 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 5 Breast carcinoma IV Positive 55 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 6 Breast carcinoma IV Positive 62 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 7 Breast carcinoma IV Positive 54 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 8 Breast carcinoma IV Positive 60 F 2008 After surgery/LP/after brain metastasis radiation and during chemotherapy 9 Breast carcinoma IV Positive 54 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 10 Breast carcinoma IV Positive 52 F 2008 No surgery/after brain metastasis radiation and during chemotherapy 11 Breast carcinoma IV Positive 65 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 12 Breast carcinoma IV Positive 48 F 2008 After brain metastasis surgery/Ommaya/ after radiation and during chemotherapy 13 Breast carcinoma IV Positive 46 F 2008 After surgery/LP/after brain metastasis radiation and during chemotherapy 14 Breast carcinoma IV Atypical 50 F 2008 After surgery/LP/after brain metastasis radiation and during chemotherapy 15 Breast carcinoma IV Positive 55 F 2008 After surgery/LP/after brain metastasis radiation and during chemotherapy 16 Breast carcinoma IV Positive 57 F 2008 After surgery/LP/after brain metastasis radiation and during chemotherapy Breast LM: breast cancer leptomeningial metastasis 1 Breast carcinoma IV Negative 42 F 2006 No surgery/LP/after leptomeningial radiation metastasis 2 Breast carcinoma IV Positive 60 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 3 Breast carcinoma IV Positive 59 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 4 Breast carcinoma IV Positive 61 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 5 Breast carcinoma IV Positive 64 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 6 Breast carcinoma IV Positive 53 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 7 Breast carcinoma IV Positive 66 F 2007 No surgery/LP/after leptomeningial radiation metastasis 8 Breast carcinoma IV Positive 54 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 9 Breast carcinoma IV Positive 60 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 10 Breast carcinoma IV Positive 63 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 11 Breast carcinoma IV Positive 66 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 12 Breast carcinoma IV Positive 60 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 13 Breast carcinoma IV Positive 55 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 14 Breast carcinoma IV Positive 56 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 15 Breast carcinoma IV Positive 44 F 2007 After leptomeningial surgery/Ommaya/after metastasis radiation and during chemotherapy 16 Breast carcinoma IV Positive 58 F 2007 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 17 Breast carcinoma IV Positive 54 F 2007 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 18 Breast carcinoma IV Negative 45 F 2007 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 19 Breast carcinoma IV Negative 60 F 2008 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 20 Breast carcinoma IV Positive 51 F 2008 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy 21 Breast carcinoma IV Positive 29 F 2008 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 22 Breast carcinoma IV Positive 69 F 2008 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 23 Breast carcinoma IV Positive 61 F 2008 NA leptomeningial metastasis 24 Breast carcinoma IV Positive 64 F 2008 No surgery/LP/after leptomeningial radiation and during metastasis chemotherapy 25 Breast carcinoma IV Positive 63 F 2008 No surgery/LP leptomeningial metastasis 26 Breast carcinoma IV Positive 59 F 2008 After leptomeningial surgery/Ommaya/ metastasis after radiation and during chemotherapy Lung to Brain: lung cancer brain metastasis 1 Lung cancer brain IV Positive 56 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 2 Lung cancer brain IV Positive 59 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 3 Lung cancer brain IV Positive 56 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 4 Lung cancer brain IV Positive 68 F 2007 No surgery/LP metastasis 5 Lung cancer brain IV Positive 69 M 2007 No surgery/LP/after metastasis radiation 6 Lung cancer brain IV Positive 71 M 2007 No surgery/LP/after metastasis radiation and during chemotherapy 7 Lung cancer brain IV Positive 66 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 8 Lung cancer brain IV Positive 63 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 9 Lung cancer brain IV Positive 60 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 10 Lung cancer brain IV Positive 59 F 2007 No surgery/LP metastasis 11 Lung cancer brain IV Positive 55 M 2008 No surgery/LP metastasis 12 NSCLC brain IV Negative 66 F 2008 No surgery/LP metastasis 13 Lung cancer brain IV Positive 62 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 14 Lung cancer brain IV Positive 64 F 2006 No surgery/LP metastasis 15 Lung cancer brain IV Positive 64 F 2006 No surgery/LP metastasis 16 Lung cancer brain IV Negative 46 F 2007 No surgery/LP metastasis 17 Lung cancer brain IV Positive 64 F 2007 No surgery/LP metastasis 18 NSLC brain IV Negative 50 M 2007 No surgery/LP metastasis 19 NSCLC brain IV Positive 56 M 2007 No surgery/LP/after metastasis radiation and during chemotherapy 20 NSCLC brain IV Positive 49 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 21 Lung cancer brain IV Positive 42 M 2007 No surgery/LP/after metastasis radiation and during chemotherapy 22 Lung cancer brain IV Positive 56 F 2007 No surgery/LP/after metastasis radiation and during chemotherapy 23 Lung cancer brain IV Positive 58 F 2008 No surgery/LP/after metastasis radiation and during chemotherapy 24 NSCLC brain IV Positive 48 M 2008 No surgery/LP metastasis 25 MSCLC brain IV Negative 54 F 2008 No surgery/LP metastasis 26 NSCLC brain IV Negative 61 F 2008 No surgery/LP metastasis 27 NSCLC brain IV NA 51 F 2010 After surgery/ metastasis Ommaya after radiation and during chemotherapy 28 NSCLC brain IV NA 66 F 2010 No surgery/LP after metastasis radiation and during chemotherapy Lung LM: lung cancer leptomeningial metastasis 1 Lung cancer IV Positive 67 F 2006 No surgery/LP leptomeningial metastasis 2 SCLC IV Negative 52 M 2007 No surgery/LP leptomeningial metastasis 3 Lung cancer IV Negative 56 F 2008 No surgery/LP leptomeningial metastasis 4 NSCLC IV NA 63 M 2010 No surgery/LP/after leptomeningial radiation and metastasis chemotherapy NA = not available, NSCLC—non-small cell lung carcinoma, SCLC—small cell lung carcinoma/

Statistical Analysis and Support Vector Machine (SVM)-Based Data Classification.

The differences in CSF miRNAs levels between groups of samples were determined using Graph Pad Prism software by Wilcoxon Signed Rank test, and two-tailed P-values were calculated.

SVM was implemented within a machine learning software package weka (Witten, “Data Mining: Practical machine learning tools and techniques, 3rd Edition”. Morgan Kaufmann, San Francisco (2011)), available on the internet at cs.waikato.ac.nz/ml/weka. In such an approach, a sample's miRNA levels were treated as independent variables and the type of cancer, if any, as a variable to be predicted. The SVM was trained and tested on such a dataset, using standard N-fold cross-validation process. In this process the SVM was trained on all samples, except for one, and tested on that holdout sample. The procedure was repeated as many times as there were samples in the dataset, hence each sample once and only once forms the holdout set. The following choices of non-default parameters working best: Classifier: SMO, kernel RBF, Complexity parameter=one for all tasks, except breast vs. lung metastasis, in which case it was 100. Ct data were used for the classification as is, with no standardization or normalization, except “1000” was used on the place of Ct values in the cases of undetectable miRNA.

The Cancer Genome Atlas (TCGA) miRNA expression microarray data for GBM patients were downloaded from tcga-data.nci.nih.gov/tcga/homepage.htm; see Hudson et al., Nature 464:993-998 (2010). The fold difference in specific signals between GBM (n=261) and normal brain (n=10) tissue were calculated for each miRNA as described³.

Example 1. miR-10b is Present and miR-21 is Elevated in CSF of Glioblastoma and Brain Metastasis Patients

To identify miRNA biomarkers for GBM, a candidate approach was used based on previous miRNA profiling data^(3, 14, 15). An additional analysis of miRNA expression in 261 GBM patients utilized The Cancer Genome Atlas (TCGA) dataset (Hudson et al., Nature 464:993-998 (2010)) and revealed a panel of miRNAs deregulated in GBM relative to normal brain tissues (FIG. 1A). Among them, miR-10b and miR-21 were the most strongly up-regulated (FIG. 1A). miR-10b is a unique molecule, as it is the only known miRNA undetectable in normal brain while highly expressed in GBM^(16, 17). It was therefore chosen as the top priority candidate. Expression of miR-10b is also associated with metastatic phenotypes of several solid cancers, including breast and lung cancers^(18, 19).

miR-10b levels were examined in the CSF samples of the study cohort patients, and miR-10b-specific qRT-PCR product was detected in CSF of 17 out of 19 GBM patients (89% cases, FIG. 1B). This is consistent with previous finding of miR-10b expression in ˜90% of GBM tumors¹⁵. miR-10b was also detected in CSF of 81% of patients with brain and leptomeningeal metastasis of both breast and lung cancer (FIG. 1B). None of the patients with various non-neoplastic neurological conditions showed detectable levels of miR-10b at 40 cycles of the qRT-PCR reaction. Raw qRT-PCR Ct values representing specific CSF levels of miR-10b and other miRNAs are shown in Table 2B. Therefore, miR-10b in CSF is a highly indicative marker of high-grade primary and metastatic brain cancers.

Next CSF levels were assessed for another candidate miRNA, miR-21, which is the most common miRNA elevated in GBM and other cancers²⁰ and also most strongly up-regulated in GBM as compared to normal brain (FIG. 1A). miR-21 levels are significantly increased in CSF of most GBM and metastatic patients relatively to its levels in the control CSF samples (FIG. 1C), suggesting that it may represent an additional CSF biomarker for both GBM and metastatic brain cancer.

The levels of three additional candidate miRNAs upregulated in GBM relative to normal brain, miR-15b, miR-17-5p and miR-93 (FIG. 1A), have been determined in a randomly selected set of several CSF samples. The levels of all three miRNAs were higher in CSF of GBM and metastatic brain cancer patients relative to the non-neoplastic controls (FIGS. 6A, C, E); however, these differences have not reached the significance and were abolished by data normalization to miR-125b (FIGS. 6B, D, F).

TABLE 2A Accuracies of classification of brain tumors by SVM analysis. Instances classified in the test sets Comparison Correctly Incorrectly GBM versus non-neoplastic 31 (91.2%) 3 (8.8%) controls Metastasis versus non-neoplastic 88 (98.9%) 1 (1.1%) controls GBM and metastasis versus 105 (97.2%)  3 (2.8%) non-neoplastic controls GBM versus metastasis 89 (95.7%) 4 (4.3%) GBM versus non-GBM 102 (94.5%)  6 (5.5%) (all others) Metastasis versus 100 (92.6%)  8 (7.4%) non-metastasis (all others) Breast versus lung metastasis 51 (68.9%) 23 (31.1%)

TABLE 2B miRNA Type # 125b 10b 21 141 200a 200b 200c Non-neoplastic 1 34.2697 UD 33.3324 UD UD UD UD Non-neoplastic 1 33.9405 UD 33.0829 UD UD UD UD Non-neoplastic 2 33.0152 UD 33.5002 UD UD UD UD Non-neoplastic 2 32.799 UD 32.9746 UD UD UD UD Non-neoplastic 3 32.9036 UD 33.707 UD UD UD UD Non-neoplastic 3 33.5036 UD 33.5222 UD UD UD UD Non-neoplastic 4 32.1067 UD 32.5033 UD UD UD UD Non-neoplastic 4 32.2493 UD 32.8214 UD UD UD UD Non-neoplastic 5 33.8516 UD 33.258 UD UD UD UD Non-neoplastic 5 35.8576 UD 32.7309 UD UD UD UD Non-neoplastic 6 32.4644 UD 28.6672 UD UD UD UD Non-neoplastic 6 32.4621 UD 28.7054 UD UD UD UD Non-neoplastic 7 31.6864 UD 35.2616 UD 37.4531 UD UD Non-neoplastic 7 32.1712 UD 35.0806 UD 37.2431 UD UD Non-neoplastic 8 32.0006 UD 32.1841 UD UD UD UD Non-neoplastic 8 31.7911 UD 31.7029 UD UD UD UD Non-neoplastic 9 34.5177 UD 30.3603 UD UD UD UD Non-neoplastic 9 35.5515 UD 30.6514 UD UD UD UD Non-neoplastic 10 32.5169 UD 32.9137 UD UD UD UD Non-neoplastic 10 32.781 UD 32.3816 UD UD UD UD Non-neoplastic 11 30.661 UD 30.635 UD UD UD UD Non-neoplastic 11 30.706 UD 30.528 UD UD UD UD Non-neoplastic 12 30.396 UD 30.993 UD UD UD UD Non-neoplastic 12 30.159 UD 31.398 UD UD UD UD Non-neoplastic 13 29.798 UD 38.9142 UD UD UD UD Non-neoplastic 13 29.469 UD 38.9142 UD UD UD UD Non-neoplastic 14 37.111 UD 36.431 UD UD UD UD Non-neoplastic 14 36.750 UD 35.824 UD UD UD UD Non-neoplastic 15 32.311 UD 33.307 UD UD UD UD Non-neoplastic 15 31.782 UD 33.483 UD UD UD UD GBM 1 28.493 35.4474 24.8591 UD UD UD UD GBM 1 28.3347 36.1669 25.0358 UD UD UD UD GBM 2 30.27 UD 28.5448 UD UD UD UD GBM 2 29.8595 UD 28.7406 UD UD UD UD GBM 3 25.5607 33.3961 22.0836 36.807 33.5488 36.6658 36.6814 GBM 3 24.3582 33.0576 22.1982 35.7105 33.2086 37.0597 37.1643 GBM 4 24.9425 37.8446 23.4126 UD 35.597 UD 34.1835 GBM 4 24.8871 37.0681 22.9477 UD 35.0309 UD 34.1049 GBM 5 34.2504 UD 33.3238 UD UD UD UD GBM 5 34.4141 UD 33.2358 UD UD UD UD GBM 6 25.9917 36.2066 21.9135 UD 35.2526 UD UD GBM 6 25.7625 36.2066 21.6147 UD 34.1246 UD UD GBM 7 29.2959 33.4857 29.3222 UD 37.1513 UD UD GBM 7 29.1532 33.1848 28.6781 UD 36.9511 UD UD GBM 8 29.7628 30.8808 33.2773 UD UD UD UD GBM 8 29.6696 30.7112 32.7008 UD UD UD UD GBM 9 29.5463 36.926 22.4494 28.5888 UD 31.3221 UD GBM 9 29.8912 38.0723 22.4455 29.173 UD 31.7444 UD GBM 10 18.8301 28.2565 21.3035 34.1768 30.673 35.202 30.9622 GBM 10 19.1781 28.3153 20.1106 35.3052 31.3501 34.5208 32.0136 GBM 11 19.0653 25.3992 19.9446 35.7793 30.3237 34.3587 35.3505 GBM 11 19.0975 25.3985 20.5917 35.4663 29.8643 34.234 36.6375 GBM 12 21.4785 29.5007 22.5529 34.3938 32.3403 36.3228 33.6589 GBM 12 21.4785 30.5404 22.0745 35.6437 32.8565 35.9838 33.3638 GBM 13 20.6069 28.0427 22.8669 38.4408 29.7108 34.4638 31.5322 GBM 13 21.1061 27.6744 22.4195 36.4015 31.1373 33.8695 32.1085 GBM 14 20.5726 29.0133 19.8893 35.0699 31.0412 35.4186 32.4751 GBM 14 20.4409 29.2476 20.1753 36.0567 31.5226 35.4393 33.3155 GBM 15 28.0429 34.4698 31.1034 UD UD UD UD GBM 15 28.3493 34.9682 31.2799 UD UD UD UD GBM 16 18.9454 29.2594 20.2101 33.9212 UD 34.7543 30.0307 GBM 16 19.0949 29.0995 19.8017 34.5306 UD 34.0056 31.1451 GBM 17 19.0563 25.713 19.6841 35.3343 28.5198 31.2043 31.0678 GBM 17 19.3106 26.0705 19.6881 35.0194 29.3597 31.4789 31.798 GBM 18 31.138 34.459 26.774 UD UD UD UD GBM 18 31.555 35.215 26.695 UD UD UD UD GBM 19 28.157 33.496 27.861 UD UD UD UD GBM 19 27.883 34.539 27.602 UD UD UD UD Breast to Brain 1 27.8174 32.0139 21.1639 29.5078 26.0618 31.4264 27.1292 Breast to Brain 1 27.2568 31.706 20.675 29.3259 26.2505 30.9209 27.7123 Breast to Brain 2 32.6303 UD 28.0095 37.1365 31.0578 32.6672 31.5072 Breast to Brain 2 32.5818 UD 27.7492 37.6775 31.0501 32.4441 31.8525 Breast to Brain 3 25.7808 31.3092 20.1414 29.1359 27.1009 30.5338 28.0328 Breast to Brain 3 25.977 31.3399 20.1774 29.2168 26.8024 30.2247 28.4686 Breast to Brain 4 31.1532 38.8239 23.4787 32.0578 26.4437 29.4728 30.6951 Breast to Brain 4 31.3755 UD 23.5862 32.8802 26.9978 29.1922 31.5229 Breast to Brain 5 29.6268 36.8038 25.6345 29.8542 24.483 27.1907 28.9925 Breast to Brain 5 30.2187 36.262 25.0105 32.3864 24.483 27.2909 29.4038 Breast to Brain 6 30.3481 UD 25.5752 30.7873 24.67 28.5216 26.9064 Breast to Brain 6 30.709 UD 27.1514 31.7873 24.7185 28.2027 26.8947 Breast to Brain 7 35.4251 36.5204 28.0536 32.7134 27.8074 30.2571 32.2786 Breast to Brain 7 35.9251 36.5204 28.2612 32.3935 28.0258 29.9113 33.1268 Breast to Brain 8 30.5423 36.5667 27.8147 32.3054 29.5245 32.3943 29.0791 Breast to Brain 8 30.1858 36.8752 27.8631 32.1674 29.9147 32.5332 28.0088 Breast to Brain 9 32.1644 UD 25.9139 31.7038 28.1264 30.3435 30.0191 Breast to Brain 9 33.1737 UD 25.8558 32.1792 28.1041 30.1035 30.2432 Breast to Brain 10 28.3774 37.1231 25.108 28.4444 27.2268 31.0834 26.2144 Breast to Brain 10 28.8228 36.1869 25.0972 28.835 26.5499 31.1109 25.9687 Breast to Brain 11 33.2952 UD 30.864 UD 33.4073 38.1796 33.7632 Breast to Brain 11 32.6806 UD 30.8002 UD 35.7065 37.0988 33.3951 Breast to Brain 12 30.044 32.846 25.180 30.020 30.641 32.699 29.391 Breast to Brain 12 29.709 34.234 25.414 30.461 30.452 32.992 30.033 Breast to Brain 13 30.368 36.826 27.307 33.816 33.117 35.072 31.908 Breast to Brain 13 30.417 36.920 27.261 33.340 32.604 35.081 32.021 Breast to Brain 14 21.508 25.708 23.920 35.289 35.603 35.800 34.705 Breast to Brain 14 21.414 25.617 23.742 36.763 35.476 38.213 34.781 Breast to Brain 15 29.378 36.876 26.886 30.667 30.539 32.789 29.405 Breast to Brain 15 29.457 36.376 26.601 30.678 30.333 32.183 29.738 Breast to Brain 16 30.966 36.324 30.592 34.492 34.035 36.778 32.881 Breast to Brain 16 30.699 37.014 30.740 34.933 33.617 36.980 32.690 Breast LM 1 30.631 35.604 28.651 35.954 35.557 UD 35.152 Breast LM 1 30.519 35.568 28.452 37.282 35.763 38.580 UD Breast LM 2 26.997 34.318 20.781 29.000 26.659 28.954 27.883 Breast LM 2 26.886 34.178 20.395 29.111 26.412 28.871 28.265 Breast LM 3 24.423 31.054 19.165 27.767 25.225 27.433 26.237 Breast LM 3 24.284 31.130 18.992 27.967 25.008 27.407 26.622 Breast LM 4 28.283 35.548 22.324 30.800 26.526 30.470 29.647 Breast LM 4 28.123 34.502 22.095 30.900 26.425 30.638 29.759 Breast LM 5 24.748 31.465 19.238 29.508 26.466 28.156 27.618 Breast LM 5 24.735 31.253 19.162 29.591 26.347 28.039 27.623 Breast LM 6 25.164 31.746 19.547 29.870 27.440 28.653 28.036 Breast LM 6 25.097 31.742 19.467 30.269 27.271 28.579 28.192 Breast LM 7 31.297 34.899 28.895 38.345 36.188 UD 28.182 Breast LM 7 31.275 34.054 28.710 38.815 36.763 UD 28.202 Breast LM 8 25.550 31.414 20.539 30.363 28.001 30.203 29.081 Breast LM 8 25.382 31.941 20.389 31.110 28.097 29.728 29.224 Breast LM 9 25.436 32.248 19.751 29.839 27.778 29.802 28.736 Breast LM 9 25.381 32.310 19.668 30.266 27.705 29.566 29.577 Breast LM 10 26.174 32.970 20.036 32.305 28.691 30.722 29.632 Breast LM 10 26.062 32.313 19.916 32.080 28.712 31.071 29.973 Breast LM 11 29.221 35.174 24.557 36.691 33.055 33.915 32.966 Breast LM 11 29.204 34.316 24.509 36.177 32.815 33.137 33.631 Breast LM 12 30.453 UD 27.958 33.654 30.871 33.833 31.953 Breast LM 12 30.371 UD 28.002 33.772 30.846 33.242 32.321 Breast LM 13 27.006 33.424 22.239 33.263 29.571 30.881 30.444 Breast LM 13 27.006 33.535 22.293 33.286 29.470 30.810 30.672 Breast LM 14 25.784 33.436 20.025 27.453 24.736 26.462 25.903 Breast LM 14 25.723 33.897 19.953 27.674 24.601 26.389 26.229 Breast LM 15 28.633 34.998 26.284 32.961 30.162 31.955 30.838 Breast LM 15 28.428 35.181 26.165 33.110 30.148 31.753 31.015 Breast LM 16 28.807 35.442 26.537 32.348 30.373 32.301 31.592 Breast LM 16 28.680 34.988 26.355 33.175 30.416 32.011 31.681 Breast LM 17 29.268 24.630 21.239 29.995 28.911 29.920 27.692 Breast LM 17 29.097 24.605 20.887 30.363 28.886 29.762 28.305 Breast LM 18 29.702 31.968 26.406 31.073 30.501 32.820 29.712 Breast LM 18 29.969 31.514 26.260 31.508 30.430 32.741 29.802 Breast LM 19 26.527 31.477 22.035 28.358 30.716 30.165 26.926 Breast LM 19 26.526 31.654 21.967 28.392 30.713 30.015 27.044 Breast LM 20 26.373 35.276 19.590 26.371 27.901 25.011 24.178 Breast LM 20 26.270 34.665 19.544 26.438 27.631 25.089 24.138 Breast LM 21 28.123 34.414 23.245 29.885 23.275 31.398 28.881 Breast LM 21 28.134 34.245 23.257 29.831 23.046 31.542 28.934 Breast LM 22 32.904 UD 29.293 34.773 34.715 36.438 33.616 Breast LM 22 33.028 UD 29.127 34.571 34.321 37.548 33.449 Breast LM 23 27.233 35.308 21.986 28.639 29.883 31.056 27.869 Breast LM 23 27.156 36.094 22.032 28.654 29.878 31.049 28.177 Breast LM 24 28.149 33.316 25.137 27.720 27.842 30.901 26.319 Breast LM 24 27.947 32.855 24.882 27.926 27.995 30.793 26.763 Breast LM 25 27.659 34.227 19.330 26.775 23.657 24.032 23.402 Breast LM 25 27.362 34.603 19.135 27.104 23.416 23.953 24.071 Breast LM 26 31.169 UD 25.420 28.289 25.468 30.137 26.360 Breast LM 26 30.721 UD 25.250 28.572 25.305 30.119 26.642 Lung to Brain 1 27.3027 31.5496 22.65115 25.3368 25.2186 28.9333 24.1757 Lung to Brain 1 27.2988 31.1058 23.05115 25.3807 25.1565 28.3453 23.8634 Lung to Brain 2 29.8443 34.8497 25.1519 32.1757 31.3363 34.9516 30.1594 Lung to Brain 2 29.7741 34.7253 24.5772 31.9565 31.6946 35.3302 29.1884 Lung to Brain 3 33.0843 UD 29.3511 34.3175 34.4514 37.0247 33.1313 Lung to Brain 3 33.5869 UD 29.4506 34.7511 34.8228 36.7855 32.3656 Lung to Brain 4 32.6941 UD 28.2911 33.9836 31.5455 33.2976 30.4481 Lung to Brain 4 32.6056 UD 27.1608 32.7802 31.2428 33.0444 30.4042 Lung to Brain 5 30.2049 34.8968 24.7768 30.4436 28.4256 30.2405 27.5537 Lung to Brain 5 29.5105 34.7725 24.0629 30.5538 28.1955 29.9892 27.1272 Lung to Brain 6 32.5851 36.6255 29.7253 35.4127 33.7658 35.5324 30.491 Lung to Brain 6 32.7851 37.4443 29.7184 35.1166 33.1176 35.0508 31.0042 Lung to Brain 7 29.261 33.4991 24.232 28.9268 28.5605 30.46 27.6959 Lung to Brain 7 28.4163 33.0663 23.8848 28.9189 28.312 30.706 27.9061 Lung to Brain 8 30.4814 34.687 22.3076 28.6553 27.6452 30.3316 29.4116 Lung to Brain 8 30.776 35.2047 21.9802 29.0701 27.6333 29.272 28.661 Lung to Brain 9 30.2956 34.349 26.8941 30.8863 29.4441 31.3527 31.2236 Lung to Brain 9 29.9115 33.5384 27.4941 31.091 29.5607 31.5945 29.1472 Lung to Brain 10 29.1638 35.0255 22.6924 29.9554 32.817 31.0666 27.3901 Lung to Brain 10 29.4353 34.4966 23.1541 30.097 32.9107 31.0331 27.2599 Lung to Brain 11 27.4463 33.4652 21.1578 26.9988 25.7732 28.9661 25.0689 Lung to Brain 11 27.3261 34.1371 20.9667 26.3149 25.4019 28.0832 24.8732 Lung to Brain 12 32.8667 UD 30.8165 UD UD UD 38.2814 Lung to Brain 12 32.2667 UD 30.3494 UD UD UD 37.08 Lung to Brain 13 34.1699 UD 24.4215 30.4942 29.2874 31.5813 31.9309 Lung to Brain 13 34.2134 UD 24.2206 30.0906 29.0842 31.5813 32.2984 Lung to Brain 14 29.293 34.571 24.394 30.789 29.544 33.057 28.864 Lung to Brain 14 29.009 35.563 24.532 30.838 29.377 32.956 28.902 Lung to Brain 15 28.914 34.550 22.560 29.644 28.600 30.866 27.167 Lung to Brain 15 28.707 34.495 22.627 29.678 28.693 30.347 27.103 Lung to Brain 16 26.601 31.991 22.155 27.351 26.558 28.982 26.586 Lung to Brain 16 26.458 32.220 22.243 27.760 26.265 28.980 27.004 Lung to Brain 17 30.365 35.322 22.837 28.904 28.364 30.994 27.650 Lung to Brain 17 30.368 35.505 22.640 28.751 27.744 31.052 27.517 Lung to Brain 18 30.310 35.762 29.548 34.882 35.961 39.607 33.730 Lung to Brain 18 30.162 37.352 29.501 35.203 35.808 38.411 34.555 Lung to Brain 19 29.630 32.016 24.964 27.431 28.617 30.526 27.507 Lung to Brain 19 29.594 31.720 24.962 27.681 28.632 30.398 27.934 Lung to Brain 20 28.500 UD 23.147 26.762 28.607 29.801 25.805 Lung to Brain 20 28.472 UD 23.183 26.857 28.429 29.778 25.829 Lung to Brain 21 26.383 33.937 21.266 29.484 30.964 31.936 28.164 Lung to Brain 21 26.398 33.081 21.299 29.664 30.766 31.886 28.331 Lung to Brain 22 27.589 36.414 24.198 31.107 33.120 35.063 30.855 Lung to Brain 22 27.681 36.387 24.163 31.499 32.544 34.379 30.925 Lung to Brain 23 27.335 33.311 20.275 26.183 27.803 29.310 26.190 Lung to Brain 23 27.203 32.897 20.198 26.497 27.698 29.155 26.421 Lung to Brain 24 31.188 33.761 24.351 30.843 31.061 32.678 30.078 Lung to Brain 24 31.066 34.498 24.576 31.006 30.770 32.639 29.865 Lung to Brain 25 25.438 33.677 22.276 27.030 26.485 28.167 25.754 Lung to Brain 25 25.257 32.734 22.333 27.055 26.320 28.058 25.845 Lung to Brain 26 27.957 35.622 26.272 30.664 29.900 32.145 28.598 Lung to Brain 26 27.770 35.349 25.912 30.721 29.989 32.029 28.710 Lung to Brain 27 27.791 35.924 23.314 30.597 29.887 31.737 29.783 Lung to Brain 27 27.719 36.972 22.870 31.188 29.900 32.049 30.955 Lung to Brain 28 27.600 34.338 22.529 26.370 28.088 31.174 26.558 Lung to Brain 28 27.498 34.905 21.968 26.742 27.800 31.009 26.244 Lung LM 1 28.652 30.282 22.137 25.738 24.665 27.190 25.600 Lung LM 1 28.606 30.400 21.843 26.250 24.557 27.097 25.948 Lung LM 2 27.795 33.788 24.948 39.425 36.261 37.184 37.034 Lung LM 2 27.934 32.653 24.846 38.606 36.606 37.702 36.898 Lung LM 3 27.478 37.812 31.801 29.974 29.569 31.303 28.059 Lung LM 3 27.310 37.200 31.664 30.034 29.446 31.181 28.566 Lung LM 4 27.588 32.726 19.656 24.357 24.419 27.413 24.179 Lung LM 4 27.627 32.723 19.472 24.376 24.369 27.213 24.289 UD = Undetermined

Example 2. miR-200 Family in the CSF is Indicative of Brain Metastasis

miR-10b is expressed in most extracranial tissues^(21, 22) (FIGS. 7A-B), and abundant in blood serum²³. However, it is not expressed in brain and not detectable in CSF of non-cancer patients. Therefore, miR-10b and other miRNAs seem unlikely to pass the blood-brain barrier under non-neoplastic conditions, and miRNAs in CSF might therefore reflect a unique miRNA signature of brain. On the other hand, miR-10b is highly expressed in breast and lung tissues, and its presence in the CSF of lung and breast cancer patients with CNS metastasis indicates that metastatic cells bring their signature miRNAs to the CSF. Based on these data, other miRNA CSF biomarkers were sought that could enable discrimination between GBM and metastatic brain tumors. Such miRNAs should be highly expressed in a primary carcinoma or tissues of its origin (e.g. lung or breast) but not in brain or GBM.

According to miRNA profiling across different tissues, miRNAs of miR-200 family are good candidates fulfilling this criteria. All members of this family are highly expressed in lung and breast tissues and epithelial cancers, including lung and breast carcinomas, but are barely detectable in brain^(22, 24) and FIGS. 8A-B). On the other hand the miR-200 family, unlike miR-10b, is not expressed in GBM and other primary brain tumors, making it a putative biomarker for metastatic brain cancer (FIG. 2A).

To explore a potential of miRNA-200 for distinguishing between GBM and metastatic brain cancer, the levels of four miR-200 family members, miR-200a, miR-200b, miR-200c and miR-141, were assessed in CSF of control, GBM and metastatic brain cancer patients. Remarkably, all four miRNAs were highly expressed in the majority of CSF samples collected from the patients with brain and leptomeningial metastasis, but not in the control or GBM cases (FIG. 2B-E). These data suggest miR-200 levels might be used for discriminating between primary brain cancer and brain metastasis.

In attempt to discriminate between metastasis from breast vs. lung cancer, miR-195 levels were assessed in several randomly selected CSF samples, since circulating miR-195 was proposed as a differential biomarker of breast vs. lung cancer²⁵. However, no significant differences were found in miR-195 levels in CSF of breast and lung cancer metastasis patients (FIG. 9). Another miRNA, miR-1 is expressed at higher levels in breast versus lung tissue according to miRNA expression profiles²² but miR-1 was undetectable in CSF of both breast and lung cancer cohorts of patients. Breast and lung carcinomas express strikingly similar miRNA repertoire²¹. However, there were significantly higher amounts of miR-200a and miR-200b (two miRNAs encoded as a cluster at chromosome 1p36.33) in CSF of the patients with breast cancer relative to lung cancer, while CSF levels of miR-141 and -200c (co-encoded at chromosome 12p13.31) were similar in breast and lung cancer cases (FIG. 2F). These data suggest that the ratios between miRNAs of two different miR-200 genomic clusters in CSF may be informative for discrimination between brain metastasis from breast versus lung cancer.

Example 3. Computational Classification of High-Grade Brain Malignancies Based on CSF miRNA Profiling

The relationships discovered between the miRNA CSF levels and diagnostic outcomes are illustrated by a simple diagnostic decision tree (FIG. 3A). The next experiments tested whether the samples can be classified into classes more accurately (non-neoplastic control vs. GBM vs. metastasis) using a “machine-learning technique” based on Support Vector Machine (SVM) concept. This technique was previously applied to a wide range of biological problems, including mRNA and miRNA expression data analysis in cancers²⁶⁻²⁸.

Various SVM algorithms were applied for classification of the samples. In one case (GBM vs. metastasis classification) a very simple linear classifier provides discrimination with about 95% accuracy. The levels of two miRNAs, miR-200a and miR-125b were used in this case as independent variables, and a linear function of these two Ct levels employed as a classifier with the coefficients calculated in the process of the classifier training.

Another case that allows for a similar interpretation is the classification of GBM and brain metastasis versus non-neoplastic controls. In that case a linear classifier was constructed that uses Ct levels of three miRNAs: miR-10b, miR-200a and miR-125b as features. Accordingly, a two-dimensional plane in the space spawned by the levels of these three miRNAs separated the space into two domains.

Linear algorithms provided satisfactory classification for GBM v Metastasis (using the formula 0.3364*miR-125b+0.0808*miR-10b+0.4578*miR-21+−0.0871*miR-141+0.001*miR-200a+0.0213*miR-200b+−0.3419*miR-200c−7.2516); GBM and metastasis versus non-neoplastic (0.0003*miR-125b+−0.0021*miR-10b+−0.0002*miR-21+0*miR-141+0*miR-200a+0*miR-200b+−0.0021*miR-200c+3.1536); GBM versus non-neoplastic (0.0002*miR-125b+0.0021*miR-10b+−0.0001*miR-21+0*miR-141+0*miR-200a+0*miR-200b+0*miR-200c−1.0849); Metastases versus non-neoplastic (0*miR-125b+0*miR-10b+0*miR-21+0*miR-141+0*miR-200a+0*miR-200b+0.0021*miR-200c−1.0744); GBM versus non-GBM (all others) (0.2468*miR-125b+0.1816*miR-10b+0.107*miR-21+0.0007*miR-141+0.0003*miR-200a+−0.0032*miR-200b+−0.1817*miR-200c−7.7752); Metastasis versus non-metastasis (all others) (0.3348*miR-125b+0.0838*miR-10b+0.4619*miR-21+−0.0902*miR-141+0.001*miR-200a+0.0284*miR-200b+−0.3482*miR-200c−7.3231); Breast versus lung (0.1592*miR-125b+−0.0003*miR-10b+0.0381*miR-21+−0.5325*miR-141+0.5346*miR-200a+−0.0014*miR-200b+−0.1282*miR-200c−1.0529). In each case, a negative result puts the sample into the first class, and a positive result puts the sample into the second class.

Similarly, various SVM classifiers were tested and the RBF kernel provided good separation between all classes of samples. The best classification accuracy was achieved using the levels of seven miRNAs: miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c as independent variables.

This analysis revealed that different types of cancer are distinguished from each other as well as from non-neoplastic control with the average cross-validation accuracy of about 90% (Table 2A). That means that the SVM incorrectly predicted the class of about one of ten previously unseen samples. This analysis suggests a possibility of computational differential diagnostics of brain cancers using miRNA profiling.

Example 4. The Origin of miRNA in CSF

miRNAs detected in the CSF of brain cancer patients may originate from brain tumor cells, from surrounding brain tissues or from extracranial tissues due to the blood-brain barrier disruption associated with tumor growth. To discriminate between these possibilities miR-10b and miR-21 expression levels were determined in tumor biopsies obtained during brain surgery and corresponding CSF samples from the same patients. A positive correlation was observed between miR-10b expression level in the brain tumor and corresponding CSF specimens, and no such correlation was observed for miR-21 (FIG. 3B). Of note, miR-10b is expressed in tumors but not in normal brain tissues, while miR-21 is elevated in tumors but is also present in normal brain^(14, 16) Taking these expression patterns into account, the data suggest that miRNA composition of the CSF is established by tumor cells as well as by the cells of surrounding brain tissues.

Example 5. miRNAs in CSF of Brain Cancer Patients as Markers of Disease Activity

To examine whether CSF levels of miRNAs reflect a disease status/activity, miRNA was studied in CSF of active GBM and metastatic brain cancer versus tumor remission cases. The disease was considered in remission if, following treatment, there were no evidence of tumor mass detected by MRI and CSF cytological analysis was negative. Neither miR-10b nor miR-200 family members were detected after 40 cycles of qRT-PCR reaction in CSF samples in any of remission cases (Table 3, FIGS. 10A-F). MiR-21 levels were significantly lower in cancer remission cases as compared to active GBM and metastatic brain cancer cases before treatment (FIG. 10B). These data suggest that miRNAs analyzed in this study may reflect the activity of brain tumors.

To further test whether the CSF levels of specific miRNAs reflect the disease status/activity and responsiveness to therapy, miRNA levels were determined in CSF of lung cancer and GBM patients longitudinally during course of erlotinib treatment. miRNA analysis was accompanied by MRI, CSF cytology, and clinical monitoring of the disease status. A NSCLC patient (patient A) developed parenchymal and leptomeningeal disease during course of treatment and medication adjustment (FIG. 4A). Erlotinib, an EGFR tyrosine kinase inhibitor, was given orally at the dose of 150 mg daily and increased at time of progression to 600 mg every 4 days and further to 900 mg (at 41 weeks) to achieve higher brain/CSF concentration²⁹, followed by a prolonged remission. The levels of both miR-10b and miR-200 members in CSF of this patient are consistent with the MRI results, rising during relapse and returning back to background levels after the increase of erlotinib dosage (significant drop by 45 weeks, FIG. 4A).

Patient B (FIG. 4B) had GBM in remission at the initial cytological CSF analysis and MRI that was interpreted as pseudoprogression. However, high levels of miR-10b, and significant elevation in miR-21 levels at later time indicated disease progression that was further confirmed by MRI, PET scan and repeat biopsy of new lesion. Patient C (FIG. 4C) had inadequate treatment due to functional status and rapidly progressed over a few weeks, which was reflected by an increase in levels of miR-200 family members.

Altogether, these data indicate for the first time that CSF miRNA levels may serve as biomarkers of brain cancer progression and response to therapy.

TABLE 3 miRNA Ct values 125b 10b 21 141 200a 200b 200c GBM 31.7864 UD 29.3547 UD UD UD 39.7125 remission 31.9339 UD 29.1258 UD UD UD 39.1993 GBM 33.5069 UD 32.0307 UD UD UD UD remission 33.8544 UD 32.6707 UD UD UD UD GBM 35.658 UD 34.5313 UD UD UD UD remission 35.5648 UD 36.6153 UD UD UD UD NSCLC 33.9462 UD 32.8533 UD UD UD UD remission 33.2768 UD 33.3858 UD UD UD UD NSCLC 28.28 UD 27.57 UD UD UD UD remission 28.28 UD 27.57 UD UD UD UD NSCLC 35.02 UD 31.35 UD UD UD UD remission 35.02 UD 31.35 UD UD UD UD Breast 28.28 33.51 27.03 UD UD UD UD carcinoma 28.28 33.51 27.03 UD UD UD UD remission

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Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

What is claimed is:
 1. A method comprising: identifying a subject who has a brain tumor; providing a sample comprising cerebrospinal fluid (CSF) from the subject who has a brain tumor; and performing RT-PCR or deep RNA sequencing to determine levels of miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c in the sample from the subject.
 2. The method of claim 1, wherein the method comprises normalizing the levels of miR-10b, miR-21, miR-125b, miR-141 miR-200a, miR-200b, and miR-200c to a level of miR-125 or miR-24.
 3. A computer-implemented method comprising: identifying a subject who has a brain tumor; providing a sample comprising cerebrospinal fluid (CSF) from the subject who has a brain tumor; and performing RT-PCR or deep RNA sequencing to determine levels of miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c, in the sample from the subject, to provide a subject dataset; downloading the dataset into a computer system having a memory, an output device, and a processor programmed for executing an algorithm, wherein the algorithm assigns the datasets into one of two categories levels of miR-10b, miR-21, miR-125b, miR-141 miR-200a, miR-200b, and miR-200c; assigning the subject dataset into the first or second category; and generating an output comprising a report indicating the assignment to the first or second category.
 4. The method of claim 3, wherein the algorithm is a linear algorithm or radial basis function.
 5. The method of claim 3, wherein the algorithm is a linear algorithm comprising: (a*miR-125b)+(b*miR-10b)+(c*miR-21)+(d*miR-141)+(e*miR-200a)+(f*miR-200b)+(g*miR-200c)−h, wherein a-g are weights and h is a constant.
 6. The method of claim 5, wherein the algorithm is: 0.3364*miR-125b+0.0808*miR-10b+0.4578*miR-21+−0.0871*miR-141+0.001*miR-200a+0.0213*miR-200b+−0.3419*miR-200c−7.2516.
 7. The method of claim 5, wherein the algorithm is: 0.0003*miR-125b+−0.0021*miR-10b+−0.0002*miR-21+0*miR-141+0*miR-200a+0*miR-200b+−0.0021*miR-200c+3.1536.
 8. The method of claim 3, wherein the first category is presence of a primary brain tumor or a metastatic brain tumor, and the second category is absence of a primary brain tumor or a metastatic brain tumor, and the method further comprises selecting a treatment for a metastatic or primary brain tumor for the subject, based on the presence of a metastatic or primary brain tumor, wherein the treatment comprises administration of one or more of surgical resection, chemotherapy, or radiotherapy.
 9. The method of claim 8, further comprising administering the treatment to the subject.
 10. A method comprising: identifying a subject who has a brain tumor; providing a first sample comprising cerebrospinal fluid (CSF) from the subject who has a brain tumor; determining levels of miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c in the first sample; providing a subsequent sample comprising cerebrospinal fluid (CSF) from the subject who has a brain tumor; and determining levels of miR-10b, miR-21, miR-125b, miR-141 miR-200a, miR-200b, and miR-200c in the subsequent sample.
 11. The method of claim 10, wherein the method further comprises administering a treatment to the subject.
 12. The method of claim 11, wherein the treatment comprises administration of one or more of surgical resection, chemotherapy, or radiotherapy.
 13. The method of claim 10, in which the levels are determined using RT-PCR or deep RNA sequencing. 